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\section*{Sammendrag}
Denne masteroppgaven presenterer en måte for å kontrollere Micromanagement i Real-Time Strategy (RTS) spill ved bruk av Potential Fields (PF) som er optimisert ved hjelp av Multi-Objective Evolutionary Algorithms (MOEA), nærmere bestemt Non-dominated Sorting Genetic Algorithm (NSGA-II). Den klassiske RTS tittelen \textit{StarCraft: Broodwar} er brukt som testplattform på grunn av sin status i AI miljøet, den detaljerte informasjonen som er tilgjengelig fra tidligere forskning og prosjekter, og open-source rammeverket Brood War Application Programming Interface (BWAPI). Det foreslåtte AI'et kontrollerer sine enheter ved å plassere flere forskjellige Potential Fields på slagmarken. Vektene som brukes bak PF'ene sine kalkulasjoner er optimisert ved bruk av NSGA-II. Dette arbeidet er et forsøk på å forbedre tidligere metoder som er gjort med PF i RTS. Resultatene indikerer at Multi-Objective Optimization er en egnet metode for å optimisere PF i RTS.

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\section*{Abstract} \todo{Rett og skriv om til å passe resten av språket.}
This thesis presents an approach to controlling Micromanagement in Real-Time Strategy (RTS) computer games using Potential Fields (PF) that are tuned with Multi-Objectve Optimized Evolutionary Algorithms (MOEA), specifically the Nondominated Sorting Genetic Algorithm (NSGA-II). The classic RTS title \textit{StarCraft: Broodwar} has been chosen as testing platform due to its status in the competitive AI scene, the amount of detailed information available from previous research and projects, and the free open-source framework Brood War Application Programming Interface (BWAPI). The proposed AI controls its units by placing several types of Potential Fields onto the battlefield. The weights behind the PFs' calculations are optimized using NSGA-II. This work is an attempt to improve on previous methods done with PF in RTS. The results indicate that Multi-Objective Optimization is a suited method for optimizing Potential Fields in RTS games.

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%\section*{Preface}
%The authors of this report are two computer scientist students at the Norwegian University of Science and Technology, with Intelligent Systems as the chosen specialization. A common interest in Artificial Intelligence and Evolutionary Algorithms in games was the driving force behind this research. 

%StarCraft was chosen as a platform to develop and conduct the tests on not only because of the practicality of the existing API (Application Programming Interface), but also because the title franchise is widely considered one of the most successful in the genre of Real-Time Strategy Games (RTS).

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\section*{Acknowledgements}
We would like to thank our supervisor, Pauline Haddow, for her steady council and feedback throughout this year, even in busy times. We also want to thank her for her open-mindedness about this project, and always being able share her expert knowledge in our discussions.

A special mention also goes to Yan Yi Look, who took the time to review our report despite having summer break.
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